Alveolar Tidal recruitment/derecruitment and Overdistension During Four Levels of End-Expiratory Pressure with Protective Tidal Volume During Anesthesia in a Murine Lung-Healthy Model.

PURPOSE: We compared respiratory mechanics between the positive end-expiratory pressure of minimal respiratory system elastance (PEEPminErs) and three levels of PEEP during low-tidal-volume (6 mL/kg) ventilation in rats. METHODS: Twenty-four rats were anesthetized, paralyzed, and mechanically ventilated. Airway pressure (Paw), flow (F), and volume (V) were fitted by a linear single compartment model (LSCM) Paw(t) = Ers × V(t) + Rrs × F(t) + PEEP or a volume- and flow-dependent SCM (VFDSCM) Paw(t) = (E1 + E2 × V(t)) × V(t) + (K1 + K2 × |F(t)|) × F(t) + PEEP, where Ers and Rrs are respiratory system elastance and resistance, respectively; E1 and E2× V are volume-independent and volume-dependent Ers, respectively; and K1 and K2 × F are flow-independent and flow-dependent Rrs, respectively. Animals were ventilated for 1 h at PEEP 0 cmH2O (ZEEP); PEEPminErs; 2 cmH2O above PEEPminErs (PEEPminErs+2); or 4 cmH2O above PEEPminErs (PEEPminErs+4). Alveolar tidal recruitment/derecruitment and overdistension were assessed by the index %E2 = 100 × [(E2 × VT)/(E1 + |E2| × VT)], and alveolar stability by the slope of Ers(t). RESULTS: %E2 varied between 0 and 30% at PEEPminErs in most respiratory cycles. Alveolar Tidal recruitment/derecruitment (%E2 < 0) and overdistension (%E2 > 30) were predominant in the absence of PEEP and in PEEP levels higher than PEEPminErs, respectively. The slope of Ers(t) was different from zero in all groups besides PEEPminErs+4. CONCLUSIONS: PEEPminErs presented the best compromise between alveolar tidal recruitment/derecruitment and overdistension, during 1 h of low-VT mechanical ventilation.

Generalized estimation of the ventilatory distribution from the multiple-breath washout: a bench evaluation study.

BACKGROUND: The multiple-breath washout (MBW) is able to provide information about the distribution of ventilation-to-volume (v/V) ratios in the lungs. However, the classical, all-parallel model may return skewed results due to the mixing effect of a common dead space. The aim of this work is to examine whether a novel mathematical model and algorithm is able to estimate v/V of a physical model, and to compare its results with those of the classical model. The novel model takes into account a dead space in series with the parallel ventilated compartments, allows for variable tidal volume (VT) and end-expiratory lung volume (EELV), and does not require a ideal step change of the inert gas concentration. METHODS: Two physical models with preset v/V units and a common series dead space (vd) were built and mechanically ventilated. The models underwent MBW with N2 as inert gas, throughout which flow and N2 concentration signals were acquired. Distribution of v/V was estimated-via nonnegative least squares, with Tikhonov regularization-with the classical, all-parallel model (with and without correction for non-ideal inspiratory N2 step) and with the new, generalized model including breath-by-breath vd estimates given by the Fowler method (with and without constrained VT and EELV). RESULTS: The v/V distributions estimated with constrained EELV and VT by the generalized model were practically coincident with the actual v/V distribution for both physical models. The v/V distributions calculated with the classical model were shifted leftwards and broader as compared to the reference. CONCLUSIONS: The proposed model and algorithm provided better estimates of v/V than the classical model, particularly with constrained VT and EELV.

Generalized estimation of the ventilatory distribution from the multiple-breath nitrogen washout.

BACKGROUND: This work presents a generalized technique to estimate pulmonary ventilation-to-volume (v/V) distributions using the multiple-breath nitrogen washout, in which both tidal volume (V T ) and the end-expiratory lung volume (EELV) are allowed to vary during the maneuver. In addition, the volume of the series dead space (v d ), unlike the classical model, is considered a common series unit connected to a set of parallel alveolar units. METHODS: The numerical solution for simulated data, either error-free or with the N2 measurement contaminated with the addition of Gaussian random noise of 3 or 5 % standard deviation was tested under several conditions in a computational model constituted by 50 alveolar units with unimodal and bimodal distributions of v/V. Non-negative least squares regression with Tikhonov regularization was employed for parameter retrieval. The solution was obtained with either unconstrained or constrained (V T , EELV and v d ) conditions. The Tikhonov gain was fixed or estimated and a weighting matrix (WM) was considered. The quality of estimation was evaluated by the sum of the squared errors (SSE) (between reference and recovered distributions) and by the deviations of the first three moments calculated for both distributions. Additionally, a shape classification method was tested to identify the solution as unimodal or bimodal, by counting the number of shape agreements after 1000 repetitions. RESULTS: The accuracy of the results showed a high dependence on the noise amplitude. The best algorithm for SSE and moments included the constrained and the WM solvers, whereas shape agreement improved without WM, resulting in 97.2 % for unimodal and 90.0 % for bimodal distributions in the highest noise condition. CONCLUSIONS: In conclusion this generalized method was able to identify v/V distributions from a lung model with a common series dead space even with variable V T . Although limitations remain in presence of experimental noise, appropriate combination of processing steps were also found to reduce estimation errors.